DNA

Thanks, Daniel. We always start with this, Ginkgo's mission is to make biology easier to engineer. I mentioned this at the last earnings call, but in 2026, our focus will be on investing to win the category of autonomous labs. I'm really excited, even since we just spoke a few months ago, this category has really been growing in attention, new companies in Silicon Valley pursuing this, a lot of interest from the AI frontier labs about the application of AI models in science via autonomous labs, government talking more about this. I do think we're onto the right track with this focus for the company. The two big ways I'm gonna be pursuing that goal in 2026.

The first is to take our services, in solutions, in data points, in cloud lab, and run them on top of our autonomous lab here in Boston that we call Nebula. That's a chance to prove out the capabilities of our system with real-world activities. The second big area of activity will be getting early adopters of autonomous labs out in the world to buy our systems like we've done already with Pacific Northwest National Labs that I talked about last time. Excited to pursue both of those, and you're gonna hear more about it from me in the strategic section. We also in the last quarter, were able to close on a deal I talked about extensively last time, which is the spin-off of our biosecurity unit into a new company called Perimeter.

I wanna say congratulations to the team at Biosecurity at Ginkgo in pulling that off. $60 million and a lot of great new investors coming into that focus really firmly in the area of defense tech and building sort of a Biosecurity prime. Ginkgo as a shareholder in that company, we're super excited to see it succeed. I think this is a really nice, as I talked about last time, opportunity both for Ginkgo to keep our focus on the autonomous labs and for the team at Perimeter to grow under their own brand with a new set of defense tech-focused investors. Our focus over the last couple of years was very much on getting these numbers where they are today, bringing down our cash burn in the company.

We guided towards this, and Steve will touch on that in his section. Again, happy to have a very strong cash position, $373 million with no bank debt, as of Q1 2026. You'll hear a little bit more from Steve on this. This sets us up very nicely. We're well capitalized to pursue this area of autonomous labs. We have these base service businesses to build on top of and the lead in developing the technology. You put all that together, and I think we're by far the best best bet in this sector. All right. I'm gonna pass it on to Steve to dig into the financials.

Thanks, Jason. Before I walk through our financials, I want to take a moment to frame an important change in how we are presenting our results beginning in Q1, 2026. As we announced in February, we entered into a definitive agreement to sell our biosecurity business, which was previously reported as a separate segment. As Jason noted, we closed that transaction on April third. The biosecurity transferred assets met the criteria under U.S. accounting to be classified as Held for Sale and the financial results reported as Discontinued Operations as of March thirty-first, 2026. This is the first quarter in which biosecurity is reflected as Discontinued Operations within our financial statements. In accordance with the accounting rules, we have and will retrospectively recast all prior periods presented to conform to this presentation.

That means the revenue, operating expenses, and cash flows previously attributed to the biosecurity business are removed from each line item of our continuing operations and cash flows as the prior period information is presented, including for Q1 of last year. The former biosecurity results are now reported as a single net line loss from discontinued operations below loss from continuing operations. To be clear, all of the financial commentary I will provide today relates exclusively to continuing operations. We will not be discussing the biosecurity business further in our prepared remarks. On April 7, 2026, for your information, we filed a current report on Form 8-K that includes pro forma financial information for fiscal years 2023, 2024, and 2025 on a continuing operations basis. Following the biosecurity divestiture, we now operate as a single segment. With that, I'll now discuss our Q1 results.

Revenue was $19 million in the first quarter of 2026, down 49% compared to the first quarter of 2025. As previously disclosed, revenue in the first quarter of 2025 included $7.5 million in non-cash revenue relating to the mutual termination of the BiomEdit agreement. Excluding this, revenue in the first quarter of 2026 was down 37% from the prior year period. It is important to note that our net loss includes a number of non-cash and other non-recurring items as detailed more fully in our financial statements. Because of these non-cash and other non-recurring items, we believe adjusted EBITDA is a more indicative measure of our profitability. A full reconciliation between adjusted EBITDA and GAAP net loss from continuing operations can be found in the appendix.

In the first quarter of 2026, R&D expense decreased 38% from $49 million in the first quarter of 2025 to $30 million in the first quarter of 2026. G&A expense decreased 35% from $20 million in the first quarter of 2025 to $13 million in the first quarter of 2026. These decreases were all driven by our restructuring efforts. Net loss from continuing operations was $76 million in the first quarter of 2026, compared to a loss of $83 million in the prior year period.

The reduction in loss year-over-year was due to our restructuring efforts. Moving further down the page, you'll note that adjusted EBITDA in the first quarter of 2026 was negative $42 million, which was down from negative $44 million in the first quarter of 2025. Since we are now only operating in a single segment, we only present a single measure of adjusted EBITDA. It is important to note that adjusted EBITDA includes the carrying cost of excess lease space, which you can see was $16 million in the first quarter of 2026. Previously, this cost would not have been included in the former presentation of segment adjusted EBITDA. This cost represents the base rent and other charges relating to lease space which we are not occupying, net of sublease income.

This is a cash operating cost that is not related to driving revenue right now and can be potentially mitigated through subleasing. Finally, cash burn in the first quarter of 2026 was $48 million, down from $58 million in the first quarter of 2025, a 17% decrease. As previously reported, in October 2025, we amended and reset the annual commitments with Google Cloud for $14 million. Resetting the commitment reduced our future minimum commitments by more than $100 million compared with the original terms, and extended the commitment term from 3 to 6 years. We paid this $14 million in Q1 of 2026, which is reflected in our cash burn for the quarter.

Excluding the payment to Google Cloud, cash burn reflects a significant decrease in the first quarter of 2026 compared to the first quarter of 2025, which was a direct result of the restructuring. Turning to guidance. As we discussed in February, 2026 is about continuing to be cost-efficient while investing in our AI robotics and software to bring autonomous labs to our bioscience customers, including the build-out of our frontier autonomous lab in Boston. We have turned the page on our pure focus on restructuring actions to focus this year not only on cost efficiency, but on investing in what we see as our opportunities while continuing to provide our customers the advanced services that they have come to expect. For these reasons, we believe cash burn best reflects our continuing services and tools and further investments in autonomous labs.

In terms of outlook for the full year, we are reaffirming our overall cash burn guidance for 2026, totaling $125 million-$150 million. This range reflects a firm balance amongst cost efficiency, continuing services and tools, and further investments we are making. In conclusion, we are pleased with our continued improvements in cash burn efficiency and our business pursuits for 2026. With that, I'll hand it back over to you, Jason.

Thanks, Steve. I'm gonna dive in on the strategic section. I'm excited to go into this today. Our mission is to make biology easier to engineer. The way we're really aiming, to solve that problem, we believe the bottleneck fundamentally is the laboratory work associated with bioengineering. I'm gonna dig deep today and talk about why autonomous labs will be replacing the lab bench. I wanna highlight, some of what we're doing with Nebula, our system, 'cause we have, some news this month, in terms of expanding that system. Finally, the services that we put on top of Nebula are Cloud Lab, Datapoints, and Solutions. These are sort of like, we call it like our Starlink, right?

If you think about SpaceX, 70% of the launches last year were actually Starlink, their own internal product. In the coming year, the ability for us to scale up an autonomous lab and showcase that you can make money on services without having people in the middle of a lab doing those laboratory services, I think is a real highlight and will help drive sales of our systems into the world. I'm gonna talk about all three, and let's dive in. Okay. I gave this analogy. I'm gonna do it again 'cause I think this is, for new folks listening on the call, it's worth understanding what we say when we say "autonomous lab" as distinct from traditional lab automation. I'm gonna give an analogy from the transportation industry.

On the Y-axis here, we have the amount of automation for a certain type of transport. On the X-axis, the request flexibility. In other words, the user's asking the transportation system to do something different, or not. For a low request flexibility and a high level of automation, that's your subway, right? It's the Red Line here in Boston. You sit down in the subway, and it takes you away. You don't have to do anything. It is high level of automation, totally automated transport, but it is very inflexible. You have to wanna go to 1 of the stops on the Red Line. Low amount of automation, high amount of flexibility, that's a car, right?

You get your hands on the wheel, your foot on the pedals, it'll take you right to your house or to the grocery store or anywhere you want to go. That's roughly what the transportation system has looked like for the last 100 years. Unless, go to the next slide, you've been to California in the last 4 or 5 years, or now L.A. or Austin or soon in Boston, and you've sat in the back seat of a Waymo, and it is amazing. It is like sitting on a subway. You don't have to do anything, it'll take you right to your house. It has the flexibility of a car. It's those two things together that sort of flexibility plus a high level of automation that mean we actually give it a new name.

We don't call it an automated car. We call it an autonomous car because up till now, you've needed a human being with our brain in the loop in order to manage that amount of flexibility into the system. If you look at the next slide, you'll see the miles traveled by cars and trucks versus subways and trains in the U.S. It's more than 99% in cars and trucks. That's not because we don't know about subways. It's that we need that flexibility to do our day-to-day lives, it's required for the transportation that humans need. Now let's go into the lab bench and into the lab. Low amount of flexibility, high amount of automation. We have actually automation in the lab. It's called a workcell.

That's a 3D schematic of a workcell we have here at Ginkgo. Companies like HighRes and Thermo and Biosero make these. They're like a subway, right? They're great. They're fully automated. They'll do an experiment without a scientist in the loop, it better be the experiment that you ordered yesterday. It's not gonna do a new experiment for you today. Low amount of automation, high amount of flexibility. We have those two in the lab. It's called the lab bench. You as a scientist are basically the human glue connecting all of these different devices in the lab together to do whatever protocol you wanna do. Again, here's the kicker.

If you look at research budgets between laboratory workcells, which are used in things in pharma companies like high-throughput screening or combinatorial chemistry or things like that, versus at the lab bench, it's about 95% plus at the bench. Again, it's not like we don't know about workcells. It's that scientists need flexibility to explore all the different hypotheses they have for discovering a new drug or developing a new crop trait or whatever type of biotechnology they're doing. This is what we're trying to build at Ginkgo. We're trying to get up to that top right corner and make a Waymo. We're trying to make an autonomous lab that has the flexibility of the bench, so scientists can order whatever experiment they want, but the automation of a workcell.

In other words, they don't have to be there to do each and every step and move the samples among the different equipment and program the equipment. They can just hit go and have that protocol run end to end for them via the automation, but whatever they wanna order that day. That's the target. Look, if you go to the next slide, the value prop here I think is very clear for getting rid of the lab bench. Massive overhead cost savings. We've heard a lot about overhead costs at academic research labs and things like that. That's really paying for ultimately millions of square feet of laboratory space. There's 50 million square feet of laboratory space just in the Boston area. You can dramatically reduce that.

You can increase the research productivity of your human scientists as we need more data from AI, and we'll talk about that in a minute. We can enable AI scientists to run these lab-in-the-loop experiments. We just announced a project with OpenAI a few months ago where GPT-5 ran our lab. That's that kind of lab-in-the-loop experiments that we're seeing increasingly also in the biopharma industry. To put a point on it, like a typical large pharma, biopharma, biotech spends $1 billion-$3 billion a year on research. Not clinical trials, spending on, you know, 1 million+ sq ft of lab benches. If you look at their spending within that or on automation, it's well below $100 million, usually much less. Frankly, I think those numbers should flip.

I think really the majority of the capital should be going towards automated laboratory work, rather than manual benches. The reason for that is, I think, a relatively straightforward calculation. On the next slide, you can see a comparison between a traditional manual lab and an autonomous lab. It's about a threefold space improvement when you take this equipment that's often very spread out in a normal human-operated lab 'cause people need to get around it and safety reasons and all these things. In an autonomous lab, you can jam all that equipment right next to each other, about as tight as you can make it for the arms to work and things like that. It's about a threefold space reduction. Then the manual labs really are just run 40 hours a week, right?

I mean, it's when people come into the lab and humans are there and they gotta be there, and that's when you can get people to work. There's now almost never multiple shifts in these types of sorta high-end research labs. It's really a 40-hour work week. Our system here in Nebula is running 168 hours a week. You know, 24/7, that's a fourfold improvement in sort of hours available for utilization of your laboratory. I think a real clear threefold, fourfold on 2 different axes. It is a clear value driver. If you go to the next slide. I think there's little question the ROI is there. I think the big question with autonomous labs is a technical one.

It's how do you get that high level of automation and the high level of flexibility, that top right corner, without a human being in the loop, right? It's the same question of the Waymo. How do you get it to navigate all these different environments and different roads without a human in the loop? If you can do it's an obvious win. If we can do this in the lab, it's an obvious win. All right, let me talk through a little bit the design constraints that we focused on at Ginkgo. On the next slide, you can see, you know, a workcell, one of those subways. The way it's designed is it's designed against a particular protocol.

If you're a biopharma company and you wanna build a high-throughput screening workcell and you call a traditional automation vendor, the first question they're gonna ask you is, "Tell me about your protocol and tell me what throughput you need it run at. How many samples do you wanna get through every week?" 'Cause they're building you a subway line. It's gonna be built to do that protocol for you. If you're a, you know, a new facility head who is opening a lab in Central Square or, you know, in Kendall Square here in Cambridge, and you are building it for a scientific lead, you don't ask that scientific lead, "Hey, what's the protocol you're gonna run in this lab that 30 of your scientists are gonna use to do work?" You say, "What kind of science are you doing?

Very specifically, what equipment do you want me to install in that lab so that your scientists can be productive over the next 3 to 5 years as they use the lab?" So it is oriented around the equipment rather than the protocol. That's a sort of a subtle point, but it has a huge amount of consequences when it comes to how you design the hardware and software that responds to this challenge. If you go to the next slide, you can see our hardware solution here is what we call our RAC carts, our reconfigurable automation carts. It's basically a robot wrapped around each laboratory device, and we have control over that environment. There's a HEPA filter on the top, which is important for a lot of biological work, where you have opportunities for contamination and things like that.

We have a six-axis robotic arm and a piece of MagneMotion track that allows you to, if you go to the next slide, Lego block these together into ultimately very large setups. We're at 50 plus right now in the lab, and it's growing quickly. I'll show you some photos in a second. We have 103 racks will be coming online just in about a week, all in 1 big setup here in Boston. If you go to the next slide, I wanna show. This is actually a video of the OpenAI protocol. We did this project with OpenAI, where GPT-5 controlled the lab, and we made a video of 1 of the samples just moving through the various racks for that protocol.

This just gives you a sense of, like, how does it work, right? You have these tracks, and we're able to move, like, our one sort of constraint on the system is that we pass things in what's called SBS format. That little rectangle you saw there is, like, a three-by-five-inch square, rectangle, and it can contain a 96, or this is a 384-well plate or 1,586-well plate. It can also carry consumables like tips and other things. The arm picks up that piece of plastic ware or samples or tips or whatever it might be, and then puts it onto a particular device. In this case, it's going, you know, just went through an acoustic liquid handler. Now it's going onto a Bravo liquid handler.

You saw, that 1st thing that got put down there was actually the plastic tips that are now getting picked up by the liquid handler. The other 2 plates are sort of sample and destination plates. We're, in this case, for the OpenAI project, we're picking up some synthetic DNA, and we're putting it into reaction mixes that were designed by the GPT-5 model at the time, right? Again, key feature here is any device that accepts those SBS plates, we can integrate into the racks. It takes us usually, like, a month to month and a half if it's a new device. We've now got 80-plus devices on there. We're adding new devices all the time. If a customer asks us for 1, if we wanna add 1 to Nebula, we just bring it online.

Now that plate's going to get shaken up and put onto ultimately this thermocycle or this final analytical device to run the qPCR reactions and give a readout on the performance of each one of those samples back to, in this case, an AI model. That data from most of the runs on Nebula is going back to a human scientist as opposed to an AI scientist. We expect there will be a mix of both as science goes forward. We will have both scientists and their agents ordering experiments on autonomous labs. Go to the next slide. A great thing about this system is it can expand.

You know, we started Nebula, I think, with about 8 racks doing NGS, like, next-generation sequencing prep for our samples here at Ginkgo and expanded ultimately, now up to over 100 racks on the system. Let's dig in a little bit. I wanna go in the next section. That, that was sort of the theory, you know, like how we designed the hardware in order to solve some of the challenges of the autonomous lab and what autonomy means. Now I wanna dig in a little bit on Nebula specifically, because I think what's really unique about Ginkgo is we're not just a hardware company. We actually run BSL-2 labs here in Boston and do scientific partnerships with some of the largest biotech, ag biotech, industrial biotech companies in the world.

We can actually show what it looks like to do real science on a system like this. If you go to the next slide, one of the things I'm quite proud of that we've been able to show in the last quarter is over 100 protocols with more than 30 of them being unique, submitted by scientists. I'll mention this, but these are not being submitted by automation engineers or experts in robotics, being submitted onto our system, Nebula, here in Boston, which has 50-plus lab devices all integrated together, where you can send point-to-point samples from any device to any other device as requested by those scientists.

There is nothing else like Nebula in the world today doing sort of open-ended science like this at this scale, with this number of unique protocols and users. It's proof that autonomous labs are feasible. I mean, there's work to do and talk about that. Things break as we are scaling this up, that's for sure. It is evidence, in my view that this is, this is gonna land. Like, we are gonna be the manually operated lab.

If you go to the next slide, I wanna walk through a few of the key things that you gotta show if you're gonna take out, you know, one of those laboratory floors at Takeda or Merck or Novartis or wherever or Bayer Crop Science or any of these companies that do a lot of laboratory work. First, you wanna connect 100 plus devices in a single automation setup. All right? It can't just be 5 or 10. You know, a scientist expects to have access to many different devices in order to do whatever protocol they might read about in a scientific paper, you know, this week. And that, I think about 100 is the right number. We've been able to this week, we'll find out. We're turning it on in about 5 days.

105, or 103 RACs all in one big setup. The reason we can do that is because of that RAC productized hardware, that cart I showed you. We just rolled in, you know, they came in off a truck, and we rolled another 50 in, and those have all gone in the actual install over the last three or four weeks. It's pretty fast to put that many new devices on a, on an automated setup. We'll see if it works. Second, we have run 10 now, like I said, 30-plus unique protocols, 100-plus different, 100-plus total protocols. That's, you gotta get in that, I think, 50 to 100 to maybe 200 unique protocols all running on the autonomous lab at the same time.

We do that with our Catalyst. This is our software. Our scheduler that we built. This is a very complicated scheduling problem. It is really easy to mess this up. Biology is very sensitive to timing. Things break all the time as we keep driving the scale up here. We are getting to do that quick cycle of debugging and improving the system. That scheduler is really the key piece of software driving that. Finally, scientists, not automation engineers, I think on a peak day on Nebula, we had 439 or so scientists submitting. That is really exciting. Like, to have that many different scientists submitting protocols on one automation system, again, I do not know of any automation system in the world that has been able to do that before.

We're able to do that in part by leveraging AI coding tools with custom harnesses wrapped around them that basically understand how to transfer a scientist's intent in human language into code to operate the autonomous lab. That is a big unlock. We're very thankful for what's going on with all the coding agents. That's a real help for improving the ease of use because at the end of the day, to make robots do something, you have to program them. To walk up to a lab bench and do your work by hand, you don't.

We have to solve this problem of we can't make it so that scientists have to become coders to do their job, and we've really just been given a gift by these AI coding tools, again, like the Codexes and the Claude Code and things like that can sit inside other tools that are specific to the automation to get this done. Those are the three big ones, and I'm pretty happy with the progress on all of them. If you go to the next slide, as I've mentioned several times now, we're going from 50 to 105 RACs by the end of this month.

It's gonna be awesome. It's a really cool system to see. People should come out and visit it. If you go to the next slide, that scheduler is not trivial. This is an example of our scheduler, I think, running 17 or 20 different protocols at the same time. Each color is a different protocol. Each row is a different device on the system. The X-axis is time.

You can imagine if you wanna add a new protocol to that and you're like, Okay, I need to use the device on row 3, the device on row 7, and the device on row 9, and I need the device on row 3 for the first 3 minutes, then I'm willing to tolerate a up to 1-hour gap, then the second device for 15 minutes, then up to a 30-minute gap, then the last device, it'll check can it fit you in, and if it can fit you in, or if it can fit you in by moving a couple other things that doesn't disrupt them in a way that breaks the protocol, it'll fit you in. That's awesome, right. That's very, very much not how the traditional lab automation, the subways work. They're running a batch.

That subway line is showing up at a certain time. You can't just jump and insert yourself in the middle, but you can with our scheduling software here. The next slide, the little green one, it's hard to see, but that column, third column over from the left over there, has the names of all the different scientists submitting. I really love this. I love that we're seeing different people submitting different orders for protocols every day. It's really exciting. Again, I think it's unique. We're also seeing a lot of energy on the U.S. government side. If you go to the next slide, a lot of new policy action here. There's the Genesis Mission, which we're fortunate to be a part of from the White House to bring AI into the national labs.

You know, there's a big motion right now where we're seeing an increasing amount of drug discovery work moving to China, from Kendall Square, I was talking about earlier, here in the Boston area. That's because simply Chinese scientists are paid a third as much. They're doing equal work to what's happening here in the U.S. Like they're, you know, they're just as good, they're just as smart. I think if we want to remain competitive, we gotta think about doing our research in a fundamentally different way in the United States.

I don't think we can just rest on our laurels of having the only smart scientists in the world in this area or at least versus China. I think that era is over, firmly over at this point. We gotta think about a new way to do it. I'm pretty heartened to see activity out of, you know, the National Science Foundation is funding $100 million for a network of cloud laboratories and autonomous labs. There's a new bill introduced by Senator Young to sort of do more of this, cloud labs and autonomous labs. Hopefully we see more here, but I'm encouraged by what we see already. If you go to the next slide, we're obviously very fortunate.

I had a chance in December to sort of ribbon cut the first 18 of our RACs going in at Pacific Northwest National Laboratory and signed a new contract for a $47 million much larger autonomous lab setup, nearly 100 RACs going in a new building in a couple of years at PNNL. This is really exciting and I think sort of highlights the direction I believe our national labs will go, our scientific research in the country. If you go to the next slide, we were lucky to give ARPA-H a tour of Nebula. We have a great project with them. You know, the work is accelerated by having these autonomous labs available to our scientists at Ginkgo.

I think this is something that makes a lot of sense for a lot of labs at the National Institutes of Health, for example, or NSF-funded labs or academic research universities. They would all be accelerated if our scientific talent could get many more of their hypotheses tested than are today due to the limitation of the manual lab. Next slide. Listen, Nebula is showcasing what is possible, and that means that early adopters are getting excited about it. We are also building autonomous labs for that left end of the chart here, the very earliest adopters, the people that are excited to try this out as a different way, as an alternative to their lab benches. We'll keep leaning in there, building those systems as that demand comes in.

We are seeing, if you go to the next slide, a lot of interest. We've had, you know, 600 plus visitors in the first quarter. I'll show it at the end. We have a great, like a little sign-up. We do tours weekly if any of you wanna sign up who are listening in. We're very happy to give you a tour. Okay. That's Nebula, and that's the dive on that. All right. Now I wanna talk a little bit about our service businesses, cloud labs, data points, and solutions, which I think of a little bit like I said, like our Starlink, right? Last year, 70% of the, you know, launches at SpaceX were Starlink. If you go to the next slide. That's a huge advantage for SpaceX.

That means they get to be creating an asset, a money-making asset in the form of Starlink, while also getting to test over and over and over again their launch platform. Their launch platform ultimately, I think in their view, is the big product, right? That they can have that sort of transportation layer to space. Today, they're 70% of the demand for that platform, right? I see a similar situation with the autonomous lab. We are able to have a big system here in Boston and basically prove out moving over our work from Ginkgo Datapoints, Ginkgo Cloud Lab Solutions, even our reagents business onto that platform. If you go to the next slide, I'm really excited we got our Cloud Lab off the ground just in the last quarter. It's really been exciting.

This is from The Times. "Do you wanna run an experiment for $39? Robots will do it for you." Go check out cloud.ginkgo.bio. You can go in the estimate tab at the top, type in whatever protocol you're interested in. It'll look up and see do we have the equipment needed to do your protocol, and if so, it'll make an estimate of what the price would be to run that protocol in a cloud lab. People are, I think, pretty surprised at how inexpensive it can be, and that is a reflection of where all the costs lie in doing lab work, which is in manual lab work done 40 hours a week, done at low equipment density, low equipment utilization in laboratories that cost a fortune to run.

That then flows through, it means all of the CRO services you order and so on are very expensive. We think we can solve that problem through automation and the cloud.ginkgo.bio, our Ginkgo Cloud Lab service is really a great way to do that. If you go to the next slide, this is what OpenAI took advantage of when we did this project where GPT-5 ran the lab. We had an awesome result. Back in February, we showed that after 6 rounds of design, we had improved the cost of cell-free protein synthesis by 40% over scientific state-of-the-art. That opened a lot of eyes. I think people weren't really, we didn't know ahead of time whether the models would even be able to design experiments and interpret data at this level of sophistication.

Really excited about that. Really excited about future work we're gonna be doing to keep proving this out with OpenAI. It's a neat line of work. I would say it's distinct from the autonomous lab. I'd call this really an AI scientist using the autonomous lab, you know, using a cloud lab to get its work done. It is also really an important thing to watch if you're following kind of how AI is changing science. On the next slide. Also excited, just in the last quarter, three new channels coming to our deliver business to our cloud lab and Ginkgo Datapoints service. Amazon Bio Discovery got launched by AWS, which is basically a platform to allow you to design antibodies. All three of these are sort of in the antibody space.

Benchling, similarly, and then, Tamarind Bio. These are Tamarind and Amazon are sort of ways for pharma companies to access these frontier bio models. If you think of things like AlphaFold, which got the Nobel Prize for Demis at Google, those that was like one of the earliest protein design models. There's many more now. They're computationally intensive, they're interesting, and they help drug discovery scientists come up with a design for an antibody or a protein for their drug. You gotta test it, right? Like, we don't know if these things work in biology unless you go into the lab.

The idea is, could you have these layers where you access the latest models and all the compute to power them, then when you're ready to do your experiment, you hit a button and it kicks the designs to a cloud lab to do it for you, and the data flows back very nicely, well-packaged, right to the model, and you can run that loop as many times as you want. That's sort of what's going on with Amazon and Tamarind, then Benchling is really the leader in electronic lab notebooks. It's a similar idea. If you're in your ELN as a scientist and you've designed this experiment, could you ultimately hit go and kick it off to a cloud lab? We partnered there with our Datapoints service again around antibodies. Super exciting to see these.

I think this is like early indications of a way that could become a norm for how scientists do their work in the future and kind of order their laboratory experiments. Okay. I'll just say a couple more quick things about data points. Really excited by the progress here, working with, you know, 10 of the top biopharma companies in the world just in the first year of running it. It's a good mix of pharma and government and even tech companies and tech bio companies. We've done a nice job, on the next slide, of really being a community leader here. We're running competitions. There's the Virtual Cell Pharmacology Initiative where we'll actually test compounds for free. People should definitely check that out if you're in the small molecule drug discovery space.

Really neat opportunities, and we host these summits and things like that. It's been good. I think AI as applied to the design of drugs, is a big area, and with Datapoints, we're sort of operating almost like a Scale AI, like creating those just big data packages that train the models. All right, next slide. We have had a long-standing business in Solutions. We have more than 250 of these research partnerships over the last 10 years. It's gotten us to work with the R&D groups of some of the largest companies in pharmaceuticals, industrial biotech, and agricultural biotech.

Uniquely at Ginkgo, it is a huge range of different kinds of research, from, you know, microbes associated with the roots of corn and trying to engineer them to produce fertilizer to mRNA therapeutics or antibody development in pharmaceuticals to enzymes for industrial biotech. Really wide range of different types of genetic engineering and biotech lab work that has happened at Ginkgo in sort of a not totally automated way. In other words, not like no people in the lab, but like semi-automated. A human interacting with a liquid handling robot and a human interacting with various benchtop devices that can, you know, take a lot of samples at once. We were sort of like not all the way to an autonomous lab, but we were doing a lot of variable work over the years in semi-automated setups.

If you go to the next slide, I'm most excited to move this kind of work onto Nebula. It is the hardest work to move, right? This is the stuff that really is that car I mentioned earlier, the lab bench. It is totally variable. It is really different. It is not just doing the same experiment over and over again like you would at a traditional CRO. If you go remember my slide, it is where 95% of the spending is going at all of our customers. They, you know, they spend a bit with us, but they mostly spend on huge internal research labs to do this kind of work. If we want to replace the manual lab bench, migrating the work from our solutions business onto Nebula is a really critical demonstration. I'm excited about the progress there.

We're trying to share that publicly. Vignettes, we bring people through. If you go to the next slide. One of the best things we do is we bring people through, show them the lab, let them talk to our scientists, see how scientists are submitting new protocols every day, and this has been really exciting to bring research leaders from You know, I've had, I don't know, three heads of pharma or ag R&D come through to visit just this year, right? To see the system. If you just wanna visit, there's the link. You really should come by.

I think Nebula and our services on top of it is a truly unique asset to demonstrate what we think fundamentally is a better way to do biotech R&D, and we would love to get it in at every company out there and replace their benches. Go to the next slide. That is the world that I wanna see. Please, if you're interested, you can email me at [email protected]. Happy to follow up and happy to take your questions now. Thank you.

Great. Thanks, Jason. As usual, I'll start with a question from the public and remind the analysts on the line that if you'd like to ask a question, please raise your hands on Zoom, and I'll call on you and open up your line. Thanks, everyone. All right. Thanks for joining us, everyone. Just a reminder to the analysts on the line, if you have a question, feel free to raise your hand and I'll call on you. We have one to start off submitted from Brendan at TD, we got over email. He has 2 questions. The first one is: How should we think about the potential impact to revenues this year from the AWS and Benchling announcements? How have the launches gone thus far, and what is baked into your assumptions for the rest of 2026 for these new platforms?

Yeah, I can take that one. Yes, we talked about AWS Benchling, the other one in that same category is the Tamarind Bio partnership as well. I'm super excited about these. I mean, this is the first time I've seen this sort of kind of like cloud layer talking directly to labs as a sales channel, I'm excited to see where it goes. It is definitely new, right? We're not like seeing a flood of inbound there. We are seeing some people reaching out to us because of the channel. That's exciting. I'm most excited that, you know, it's starting around antibodies, right? That's just kind of naturally there's a number of these AI models associated with antibodies and so on, because there's a few different providers that'll do these antibody services for you.

What I'm most excited about is with our cloud lab, we're not limited to test an antibody binding, right? If you look already on the, I don't know, 8 or 9, 10 protocols we've posted, and we're posting a new one every week. It's a pretty wide variety of stuff. We're doing mass spec, metabolomics, all kinds of things. So you can come and ask for a protocol on Ginkgo Cloud Lab, we'll add it. I'd love that to turn into a channel straight from an electronic lab notebook or whatever, where a scientist is like, "This is the protocol I want. Price it." You get a price back from, you know, cloud.ginkgo.bio, and then you go run your experiment.

I think that feels a lot closer to AWS and sort of like what we saw as successful with cloud compute than where these are today, which is really much more just in a more narrow lane around antibodies, which I think is an exciting place to start. I am super excited to fan that out. I think that then it could become really quite an interesting channel and something that scientists just don't have access to today. You know, at the end of the day, you can't get custom stuff done. I think that's what I'm most excited about there.

All right, next question from Brendan. What are you hearing on Ginkgo Datapoints and the collective AI-driven offerings with Ginkgo as especially attractive for customers as biotech and pharma companies continue to roll out their own AI capabilities? In other words, what kind of demand dynamics are you seeing here? Are there any potential revenue funnel unlocks we should watch for over the coming quarters from this part of the business?

We launched Ginkgo Datapoints, what was it, a year and a half ago now. To have, you know, 10 the top pharma companies as customers now is really exciting. I think the revenue unlock is just repeat business from those customers. We are starting to see that. What we saw was sort of like pilot project, data gen project, and then now you've got, again, because you are seeing people trying to build in-house models. Now remember, like these are not reasoning models. These are not like in-house versions of Claude or Codex or, you know, or, you know, Opus or whatever, or GPT 5.5. They are models trained on biological data, so they're much more specialized.

I do think it makes sense actually in the field that you're going to see a lot of people having their own datasets, their own models that are sort of tuned-up versions maybe of various protein models. That's not going to be uncommon at all. Much more common than I think you'll see in the reasoning model and coding space because these things are very different, and people have different datasets. I'm sort of hopeful as the people are building these models, we'll keep seeing this sort of repeat demand as they're like, "Okay, I found one. I like what I'm seeing in terms of return on data and performance of my internal model. Give me more data." That's the revenue unlock. The more of that we see, I think we become sort of like a default provider.

That's certainly what happened with Scale AI and other places in the early days of image models and then language models. When people saw, "Oh, I'm seeing performance increase with more data," they turned around and bought more data. That's what we're going to be watching as these protein models and it does not just protein, other types of models come out too in the future. I think that's the lane for Ginkgo Datapoints.

Sort of on the theme of AI, there was someone who was on X who asked us a question. I think this is sort of based on our project with OpenAI. How much efficiency improvements after using GPT 5.5? Any idea for space left for improvement? Will this be a transitional factor?

We did this project, just to remind, that we announced back in February with OpenAI as our first project with them, where we had GPT. It was actually not 5.5, it was 5, 'cause we started much earlier, and that was when that was the model that was out, and we kinda kept the same model through the whole thing for, like, more scientific paper purposes. We were able to show over a series of 6 rounds of running the model with 100 384-well plates designed by GPT 5 per round, a 40% improvement over state-of-the-art in the scientific goal we were trying to achieve. I think there's real interesting questions. A. How much further could you push that?

Like sort of, you know, what does actually diminishing re-returns look like in some of these scientific areas? Can the model have sort of breakthrough ideas that create really new ways of doing this TBD? As the models have gotten better-Yeah, would 5.5 be better than what we got with 5, right? I think that's all gonna be exciting stuff to test. We're excited to do more with OpenAI, and we're planning to. I think this is open terrain in terms of how good the reasoning models can be at basically experimental design and experimental analysis. Those are the two things it's really doing. It's like, "Here's an experiment I wanna run. Give me back the data, Cloud Lab, you know, autonomous lab.

Give me back what are the results of my experiments I just designed, and then I'm gonna analyze them and design more experiments." We'll see. You know, I think that's real exciting to watch what it's gonna be capable of there. It's a new way to do science. It really is. Like, I won't belabor this too much, the access to a model like that plus an autonomous lab can let individual scientists operate closer to how a principal investigator of an academic lab or a head of a drug discovery group who has a lab of 8 people or a lab of, you know, 30 people and is sort of assigning hypotheses to different people and kind of pursuing that over time.

An individual could push that out for probably close to the same cost as they're currently costing to be themselves at a lab bench, in terms of their utility costs and everything else, and utilization, low utilization of equipment. They could push out 5 agents on top of an autonomous lab to go pursue a bunch of experiments. That is real exciting if that works. I think it really fundamentally changes the rate we can do science. That's why you see the Genesis Mission in the U.S. investing in this sort of stuff because their goal is to 2X the output of U.S. science. These are the ways that that'll do it. Our science-based industries, of which pharma's the biggest, will be completely changed by this. If you can two or three times the rate, no question about it.

Our next question is really a bundle of questions from DK who's writing from South Korea. These questions are all about how the move onto Nebula, our autonomous lab, has sort of changed the science that we're doing. The questions are, how does the use of Ginkgo's automated lab affect overall costs? Are there meaningful differences in speed, for example, turnaround time for experiments? Have you observed improvements in success rates, reproducibility, or scalability since moving to the autonomous lab?

Yeah. On cost, I tried to touch on this a little bit in the talk, but I think, like, the clear ROI, not just for us, but for any one of our customers looking at an autonomous lab, is about a threefold reduction in space utilization compared to a manual lab and a four-fold increase in the time. In other words, like, the amount of time the lab is being used to do lab work, right? From that 40 hours to 168, 24/7 week. That's really those improvements is where it's gonna yield the cost reduction. That is a huge amount, like, 'cause those are really the two, like, sort of people time and space time are the two big things we spend money on in research. On the speed front, yeah, it's interesting.

An individual protocol doesn't really get shorter, like, than necessarily you would do it at the bench. You can imagine ways to do that in the future, rebuild protocols differently. The first thing scientists are gonna do is just take work they're doing at the bench and move it onto the autonomous lab. In that world, it does not need to get faster in terms of, like, end-to-end time for the protocol. However, it, in practice, can get faster 'cause you can start a protocol at 4:00 P.M. in the afternoon where you never would have planned to spend the next seven hours in the lab, kick it off, and have the thing run overnight.

In that world, you took an experiment that you would've started tomorrow at 10:00 A.M. and start it at 4:00 P.M. and have the results by tomorrow at 8:00 A.M. or 10:00 A.M. That can shave a whole day off. I think you will see actually a massive speed up because scientists will start taking advantage of the 4x more time that they have available every week. If they plan it right, you know, in theory, you could see a 4-fold improvement in a lot of the times, depending on how serialized your experiments need to be. I think that's really exciting, and our scientists at Ginkgo really like that. On the just sort of, like, improvement and, like, I would say, I would call this, like, the quality of the experiments.

I think reproducibility is inherently advantaged on automation, and that's mainly to do with, like, the audit trail. Like, you kinda. If an instrument errors, if a liquid handler makes a mistake, you know, like, These are all tracked. You kinda know, like, those experiments that you don't catch at the manual lab bench, you catch if there's such a mistake on the autonomous lab. If you saw a really, wow, that's a surprising result, you might go back, look at your experiment, and say like, "Oh, oops, I see what I did there. I, like, designed this experiment in a way that was, like, a little silly, and that's actually what's giving me this result." As opposed to assuming you did the experiment you wanted to do and that was the origin of this, like, amazing result you got.

That's a common thing that can happen for no nefarious reasons for scientists at the bench. I think that you will see a big improvement in reproducibility. The other thing that got brought up there was throughput. Yeah, the throughput increase is gonna be insane. I think people are surprised when they go to cloud.ginkgo.bio, which I encourage people to do, and type in a protocol and see how much it costs. 'Cause I'm basically pricing that protocol based on reagent use and equipment time and a markup on that, and it is not the insane cost that you have when you have a whole team doing this work at the bench. It's just not.

Like if scientists really understood just how low-cost each sample could be in an experiment, in order to do many more, they just hit a button rather than have to slave in the lab for 3 days doing 1,000 experiments, they're gonna just order 1,000 experiments. I think you will see an explosion in the amount of data, and this is 100% what happened in every other field that's ever been automated. Right? It's like the beginnings of the automation of computation, right? Like when we went from slide rules to automated computation, an explosion in the amount of compute you use and a massive increase in the ROI from what people who understood how to design computation could do.

That's what I want to do for the scientists, for the drug discovery leads when they have access to an autonomous lab compared to the ROI and the throughput that they can get out of manual labs. It's just going to be no comparison. Yeah, I think all three you're going to see big gains on. The cool thing is we're going to keep showing this on Nebula. We, you know, just had a, again, head of R&D through today, and we went through with his team and showed all the gains and it's, yeah, it's real exciting right now.

I think we'll end on a note kind of related to that, which is, you guys mentioned in the call, you've mentioned other places, that you're trying to get to 100 RACs. When do you actually expect to get there?

It's been pretty fun. We're here to put some behind the scene videos up. We have been installing racks for the last 3 weeks here at Ginkgo. They just showed up on trucks, built by our team in Emeryville. We just added the additional 50. They are all fully connected now, and the lab just took a tour of it. It's insane. We can run them now, like the original system is running, and now the new 50 is running, and there is a connection between the 2, and that connection is gonna get turned on, I think, on the 14th. I don't know. Next week. It is imminent. Really excited to see it all come together, but we already have it up now running as 2 separate loops.

To put in 50 new pieces of equipment in 3 weeks, again, these are just things that no one's ever done in laboratory automation. I do think we are doing a very unique thing here at Ginkgo. That's the bet. That's certainly what I'm leaning in on the company. It's what we're investing our capital into. It's where our new customers are coming from. If you like that idea, I think now is a really exciting time to get involved with the company in any way. Yes, we're gonna be at 100 next week. 103 or 5. I gotta count 'em up.

Thanks. All right, if you wanna follow us on that journey, you can go to X or LinkedIn, Instagram and keep watching. We'll have a lot of content coming about the unveiling of the new full system. As always, if you have questions, you can reach out to us at [email protected]. Thanks so much, everyone. Until next time.

Thanks, everybody.

Good evening. I'm Daniel Marshall, Senior Manager of Communications and Ownership. I'm joined by Jason Kelly, our Co-Founder and CEO; and Steve Coen, our CFO. Thanks, as always, for joining us. We're looking forward to updating you on our progress. As a reminder, during the presentation today, we will be making forward-looking statements, which involve risks and uncertainties. Please refer to our filings with the SEC to learn more about these risks and uncertainties, including our most recent 10-K. Today, in addition to updating you on the quarter results, we're going to provide insight into the autonomous lab, how we believe it will transform biotechnology and how we plan to commercialize autonomous labs going forward. As usual, we'll end with a Q&A session, and I'll take questions from analysts, investors and the public. You can submit those questions to us in advance via X, #ginkgoresults or through e-mail, [email protected]. All right. Over to you, Jason.

All right. Thanks, Daniel. So Q4 was really a breakout quarter for us in sort of defining and really leading in the category of autonomous labs. And so you're going to hear a lot from me about that in the future. But I want to start by saying our mission remains to make biology easier to engineer. But in 2026, the technological focus for the company and really the business focus is going to drill down on investing to win in this category of autonomous labs. And this is really a part of what I see as an emerging movement around robotics and AI and autonomy that's coming to a lot of sectors in the economy. And I think we happen to be in a sweet spot in bringing that into really a high-value area around laboratory research that there's an increasing amount of excitement about, and I intend to win that. All right. So how are we going to do it in '26? So first, we want to focus our investment in our platform into that area primarily. And I'll talk in a minute, but we mentioned in our recent earnings announcement just now that we'll be divesting our Biosecurity business. That allows me to focus Ginkgo's investment and our dollars really into autonomous labs and bring in other new investors to invest alongside us into Biosecurity. So that's focused in investment. Second, internal to the company, we want to demonstrate the capabilities of our large autonomous lab here in Boston. And the way we're going to do that is we're going to start to systematically decommission our lab benches, our walk-up automation, our work cells, the way that we've traditionally done our R&D services over the last 10 years and move more and more of that work on to a single large autonomous lab that's software controlled here in Boston. And the reason I want to do that is that serves as a demonstration to the Mercks and the Takedas and the Pfizers and all the folks who have huge investments in traditional manual laboratories that it is possible to take open-ended research and run it through a large autonomous laboratory system. And so I think that's really fundamentally the most important work we're doing this year. And then finally, I want to book sales of autonomous labs. So you're going to hear in one of our big announcements from last quarter that we did a $47 million deal with Pacific Northwest National Labs. So I want to sell autonomous labs to National labs like that DOE deal, but I also want to sell them to biopharma. I want to sell them to research universities. And so that sort of bookings and landing new deals is the other thing we want to do in 2026 in this direction. I do want to take a minute and talk about that Biosecurity divestiture. So you might remember, over the last 5 years, we've invested a ton of energy into this space. This really came about starting during COVID, because we honestly just saw a need. COVID was sort of a global scale biological disaster, and we felt we should lean in and help where we could. The niche that we found in that moment was doing really monitoring for -- so not diagnostic testing, but rather monitoring testing in order to reopen congregate areas and in particular, reopening schools here in the United States. So I'm really proud of this is a decent sized business for us. But really importantly, we helped open 5,000 schools nationwide. And this is one of these like really political topics. And I think what's neat about technologies, you can sometimes find a third way between and one end at the time, which was hey, we really should be closing the school, [ standard ] for teachers. We care about spreading disease. And then on the other side, hey, this is hurting kids, and we need to open the schools and everyone should just go back and whatever comes comes. And there was a third way, which was why don't we open the schools and have persistent monitoring so that if an outbreak starts to happen in a school, you can send 2 or 3 kids home and stop it. And that's exactly what we want to build at a nationwide level and what's continued after COVID in our monitoring at airports that we do in partnership with the CDC, looking for viruses in the wastewater of planes and other inputs, both here and internationally in places like Doha and Qatar, at the airport there. And so that sort of identify it, put it out, put that fire out before it spreads is something that's needed nationally and globally for the U.S. to be secure. The other thing that's been happened in that period of time, you might have noticed, companies like Anduril, Palantir, Shyam Sankar, our Board Chair, is the CTO of Palantir. This sort of defense tech sector really exploded over the last 5 years. And so there's been increasing interest from pure-play investors in the defense space who want to see next-generation sort of biodefense primes. So again, these are companies that would be focused on serving the government and others on biodefense needs directly. That's very exciting because it means there's lots of new capital interested in that. But to my point earlier, where I want Ginkgo to focus very clearly in 2026 is on autonomous labs. And so one of the great things that happened was we got a lot of inbound from these types of investors, and we saw an opportunity to say, all right, why don't we share in the upside of biosecurity by taking that business unit in the company, spinning it off, taking it private, bringing in investment from some of these great investors. Ginkgo will still hold a minority position in that. So we get a piece of the upside of what we've built, but the investment needed to build that biosecurity prime doesn't need to come from the $430 million, as I'll mention in a second that we had on our books at the end of the year. We can focus that into autonomous Labs. So I think this is a win-win all around. I also think bringing in these types of great folks that we have coming into the private entity are really going to open doors with the defense sector and so on and having it be a sole branded biodefense company, it's the right time. So I'm super excited about this. I think it's -- I want to give again credit to the biosecurity team at Ginkgo, who did absolutely amazing work through Ginkgo -- sorry, through COVID and now has a real opportunity here, I think, to build a generational business coming up in the defense sector. Okay. Last point I want to make before I hand it to Steve. So again, I think tremendous work over the last 2 years. We sorted 2 things at the same time. We dramatically cut back spending as we saw sort of a downturn in the biotech sector and a lot of our customers pulled back on outsourced large R&D projects, which was really our bread and butter here at the company over the years. Because of that, we drew down on our spending and pretty substantially. So in fiscal year '24, we were at $383 million and just last year, $171 million. So a 55% reduction in our annual cash burn. That sets us up very nicely. You're going to hear from Steve on our target for cash burn for this year, even with the investment, our focused investment in autonomous labs and moving that investment in biosecurity into a separate private entity, we're actually able to do better than what we spent in '25. But that, I think for investors is important to understand where we're at from a cash position and how we've done a really nice job getting cash spending under control as we continue to make investments and get in the right place at the right time with autonomous labs. All right. So I'm going to hand it to Steve to dive in a little more on the financials.

Thanks, Jason. I'll start with the Cell Engineering business. Cell Engineering revenue was $26 million in the fourth quarter of 2025, down 26% compared to the fourth quarter of 2024. In the fourth quarter of 2025, we supported a total of 109 revenue-generating programs. This represents a 4% decrease year-over-year, primarily attributed to ongoing program rationalization as part of our restructuring activities. Turning to the next slide. On a full year basis, Cell Engineering revenue was $133 million in 2025 as compared to $174 million in 2024. As previously disclosed, revenue in the first quarter of 2025 includes $7.5 million of noncash revenue from a release of deferred revenue relating to the mutual termination of the BiomEdit agreement. In the third quarter of 2024, Cell Engineering revenue included $45 million of noncash revenue from a release of deferred revenue relating to the mutual termination of the Motif FoodWorks agreement. Excluding these impacts, Cell Engineering revenue was $125 million in 2025 and $129 million in 2024. This decrease was primarily driven by customer program rationalization related to the restructuring, as all discussed previously. The Biosecurity business generated $7 million of revenue in the fourth quarter of 2025 and $37 million of revenue in the full year 2025. It is important to note that our net loss includes a number of noncash and other nonrecurring items as detailed more fully in our financial statements. Because of these noncash and other nonrecurring items, we believe adjusted EBITDA is a more indicative measure of our profitability. A full reconciliation between segment operating loss, adjusted EBITDA and GAAP net loss can be found in the appendix. Cell Engineering R&D expense decreased 44% from $50 million in the fourth quarter of 2024 to $28 million in the fourth quarter of 2025. For the full year 2025, Cell Engineering R&D expense decreased 42% from $272 million in 2024 to $159 million in 2025. As reported last quarter, the full year 2025 period, R&D expenses included a $21 million shortfall obligation related to our multiyear strategic cloud and AI partnership with Google Cloud. In October 2025, we amended and reset the annual commitments for future years and settled the shortfall obligation for $14 million. Resetting the commitment reduced our future minimum commitments by more than $100 million, compared to the original terms and extended the commitment term from 3 to 6 years. Cell Engineering G&A expense decreased 40% from $20 million in the fourth quarter of 2024 to $12 million in the fourth quarter of 2025. For the full year, Cell Engineering R&D -- I'm sorry, G&A expense decreased 51% from $115 million in 2024 to $56 million in 2025. These decreases were all driven by our restructuring efforts. Cell Engineering segment operating loss was $17 million in the fourth quarter of 2025 compared to a loss of $38 million in the 2024 period. For the full year 2025, Cell Engineering segment operating loss was $96 million compared to a loss of $219 million in 2024. The lower loss was directly related to our restructuring efforts, while partially impacted by the matters previously mentioned. The Biosecurity segment operating loss improved 60% in the fourth quarter of 2025 compared to the 2024 period. And the Biosecurity segment operating loss improved 38% in the full year 2025 compared to 2024. Moving further down the page, you'll note that total adjusted EBITDA in the fourth quarter of 2025 was negative $36 million, which was down from negative $57 million in the fourth quarter of 2024. Total adjusted EBITDA for the full year 2025 was negative $167 million, which was down from negative $293 million in 2024. Again, the period-over-period declines can be attributed to our restructuring efforts, while partially impacted by the matters previously mentioned. Turning to the next slide. We show adjusted EBITDA at the segment level to show the relative profitability of our segments. The principal differences between segment operating loss and total adjusted EBITDA relates to the carrying cost of excess lease space, which was $54 million in 2025, and this carrying cost was $15 million in Q4. The cost represents the base rent and other charges related to leased space, which we are not occupying net of sublease income. This is a cash operating cost that is not related to driving revenue right now and can be potentially mitigated through subleasing. And finally, turning to cash burn. Cash burn in the fourth quarter of 2025 was $47 million, down from $55 million in the fourth quarter of 2024, a 15% decrease. Cash burn for the full year 2025 was $171 million, down from $383 million in 2024, a 55% decrease. Cash burn does not include the proceeds from the ATM issuances or certain cash restrictions. The significant decrease in cash burn was a direct result of the restructuring. Turning to guidance. In terms of the outlook for 2026, as Jason has mentioned and we will go into further, 2026 is about continuing to be cost efficient while investing in our AI robotics and software to bring autonomous labs to our bioscience customers, including the build-out of our frontier autonomous lab in Boston. We have turned the page on our pure focus on restructuring actions for the last 2 years to focus this year not only on cost efficiency, but on investing in what we see as our opportunities while continuing to provide our customers the advanced services they have come to expect. We will also close our transaction for the Biosecurity business as announced and disclosed. For these reasons, in 2026, we will not be providing revenue guidance as we believe cash burn best reflects our continuing services and tools and further investments in autonomous labs. For 2026, our overall expected cash burn guidance is to be in the range of $125 million to $150 million. This range reflects a firm balance amongst cost efficiency, continuing services and tools and the further investments we are making. In conclusion, we are pleased with the continued improvements in cash burn and cost reductions in 2025 and are excited for what will come in 2026. And with that, I'll hand it back over to you, Jason.

Thanks, Steve. Okay. So before I jump into my section, I want to spend a minute talking a little bit more about what Steve was talking about there at the end in terms of our guidance for the year, how we're going to be guiding on cash burn rather than on revenues and sort of why we're doing that. And so this is in line with my theme for this earnings call, which is Ginkgo's focus. So one thing is we want to be focusing on investing in the right things. And so we're -- I believe, again, it's important for our investors in Ginkgo to understand what we're doing with our cash supply, how fast that's being spent down, what we're spending it on. And again, the highlight here is we are spending it very deliberately on autonomous Labs, and we're doing it in a controlled way. We're hopefully spending substantially less than we spent in the last year and our relative position there to our cash file, it looks pretty good. So from my standpoint, we have a solid margin of safety as we're investing to lead in this area of autonomous labs going forward. But the second thing we need to keep focus is our attention within the company. And so the majority of our revenue today does come from our R&D services. We love serving those customers. I'm hopeful we grow those services. But as I mentioned earlier, the focus of the team in 2026 is not on hitting a short-term revenue target around a service run on top of our autonomous lab to make sure we hit a target or trying to predict exactly what that revenue is going to be over the next 12 months. What I want their focus to be on is decommissioning all of the different labs here at Ginkgo and moving that work onto our autonomous lab so that we can show all of our customers that this works, that autonomous labs can be a true replacement for the humongous spending they have across their manual laboratories in both biotech and academic science. That's the main event. And I felt that, again, continuing a focus on revenue targets and things like that was going to take people's eye off the ball. And I also think it sort of takes away from a long-term orientation, which I think is going to be critical for Ginkgo. So that's why we made that decision. Happy to talk more about that in questions or otherwise, but just so you know where it's coming from. All right. Okay. So as I said, our mission is to make biology easier to engineer. We had 3 really amazing things happened last quarter. So I was going to run through them. So first, we had an announcement of a project we've been working on for the last 6 months with OpenAI. This is their blog post about it, where we talked about connecting GPT-5 as sort of an AI scientist, so doing the work of a scientist designing experiments, except they would submit those experiments to our autonomous lab here in Boston. The lab would conduct the work, send that data back to GPT-5, and then over the course of 6 rounds of doing that, we were able to beat state-of-the-art on a pretty complicated sort of scientific -- experimental scientific challenge in cell-free protein synthesis by 40%. What I think is cool about this is, number one, the sort of views on this X post where they announced Codex on the same day, about equal to what they saw with Codex, right? So I think there is really a lot of excitement right now in how reasoning models can enter the physical world, all right? I'm going to talk in a minute about that in the area of transportation where like Waymo's have brought them into the physical world. But I think we really stand to be the ones to bring AI into the physical world of the lab. We are absolutely in the pole position on that. So I'll talk more about that in a second. Second, we were -- I had the absolute privilege to do a press conference with the Department of Energy Secretary, Wright up at Pacific Northwest National Labs in Washington, where we announced in December that the first 18 robots that we were installing for PNNL as part of the Genesis project. This is a new project out of the White House to bring AI into science and AI into the national labs in particular. But alongside that ribbon cutting and the Secretary got to sign one of our system, you can see him signing it there. We also announced a new $47 million contract with the Department of Energy to build a 97 robot, 97 RAC autonomous lab at that same site in PNNL in the future. So really exciting, and I think this showcases that autonomous labs are of interest to the federal government, which is the other big pool of research spending. So a place like the NIH is spending $40 billion a year on lab work, frankly. And that's pretty close to what you're seeing in the pharma companies as well. So those are your sort of big pools of spending. And so I think it's important to see it coming from the federal government as well as from pharma companies. Last but not least, we had SLAS. This is the Society for Laboratory Automation and Screening Conference. I was just at the conference center. It's about 5 minutes away from here, very fortunately for us at Ginkgo. So we hosted tours of Nebula, our now more than 50 rack RAC lab set up here in Boston. We had 590 people come through, and it was very eye-opening to see what a difference it made for people to see a lab like this actually doing real science during the day, right, like people coming in and just seeing what our scientists were doing with it. It was eye-opening for them. And so I think this makes it more and more clear to me that we're making the right choice with this focus in '26 on really driving the further expansion of the system. We're going to go from 50 RACs to 100 RACs by H1. That's the sort of stuff I want you to be following. How quickly are we able to expand that? How quickly are we able to add more of our work onto that system because that's exactly what our pharma and national lab and university research leaders are going to be looking at to see if they want to buy a system like this. Okay. All right. So now I'm going to do a deep dive into autonomous labs because, again, I think this is really our focus, certainly in '26, and I think the technological foundation for the company over the next decade. So I'm going to talk about [indiscernible], what is an autonomous lab? Why is it going to transform biotechnology? Secondly, what does it look like very specifically? Like what do you need to have the lab be able to do in order to deliver biotech R&D? And then finally, how are we going to bring it to market? And the 2 ways we're doing that is, one, we'll build one for you like we did at PNNL. Two, the beautiful lab that you just saw pictures of, we're able to run that sort of in a cloud service model through our R&D services and new services we're adding coming up. All right. Okay. So here's the analogy I like to give. I talked -- give a talk at SLAS, and I [ did talked to ] this with a lot of people. I think it's a good one, all right? So I'm going to start in the transportation industry to help explain what I consider autonomy to be. So if you look at this chart, on the y-axis, you have the amount of automation, all right? And then on the x-axis, you have the flexibility of the request from a user to that automation that it's willing to tolerate. So in transportation, if you have a low amount of request flexibility and a high amount of automation, that's a subway, right? You sit down in the back of a subway and it just takes you away, right? You're not having to do anything. It is fully automated. But you better want to go to one of the stops on that subway line because it's not going to take you to your house or the grocery store or just wherever you want to go, it's on rails, all right? So it's very inflexible. Now low automation, high amount of request flexibility. That's a car, right? You put your hands on the wheel, your feet on the pedals and you can drive it straight to your front door or to that grocery store, right? And those 2 poles is basically what the transportation industry has looked like for the last 100 years. Forward slide, unless you've been in San Francisco over the last 4 or 5 years and you see these driving around. So this is a Waymo. You sit in the back seat, just like sitting on a subway seat, you do absolutely nothing. It takes you away, except unlike the subway, it will take you right to your house, right to that grocery store. So it has the flexibility of a car, but the automation of a subway. And that's such a surprising thing that we're giving it a new word. We're calling it autonomy, all right? And I think you will see this replicate. When you're seeing all this interest in like humanoid robotics and all this like, there's a huge amount of investment going into it right now. What we're trying -- what's happening on a broad investor level is the industrial revolution was essentially the application of automation and systematization to all of the tasks that were like low flexibility required, like back to the loom, right? Everything that wasn't that required a lot of flexibility, we kept manual. And what's happening now is the AI models are getting good enough, the software is getting good enough to allow automation to be applied to flexible things, and we're going to see how far we can push that. And the more you can push into flexibility, the bigger the opportunity there is for robotics. And so we're going to drive that change in labs. Now last point, this is the kicker. If you were to look at the split between miles traveled on trains and subways versus cars and trucks in the United States anyway, it's 99% cars and trucks. because you need that flexibility to go places, right? It's a requirement. It's not like we didn't know about rails. They just did not tolerate the flexibility needed. All right. So let's look in the lab. Low amount of flexibility, high amount of automation, so up where that subway was on the last slide. We do have that actually. We call it automation work cell. And you can buy this from companies like [ Hirose Bio ] and Biosero and Thermo Fisher. And basically, you tell them what protocol you want and they build you a work cell that will run that protocol for you. And it's great. It's totally end-to-end. There's not a person in the middle. It's fully automated. It's at top of that chart, but you better be asking for the same protocol that you asked it to do yesterday because it cannot handle variety in the request from the scientists that are using it. Drop down that automation line and go over on request flexibility. So not automated, but very, very flexible. That is the lab bench. And we've had it for 100-plus years. It lets you do whatever experiment you want and the scientists, the human scientist in the middle is what's providing the flexibility, all right? And that's what the system has looked like. We've had work cell automation for 40-plus years now that we've been kind of those 2 poles for the last 40 years. And much like research -- or sorry, much like transportation, I had a couple of heads of R&D and 2 pharma companies over my house during SLAS for dinner. And I asked the question, what's your spend between work cells and lab benches? And they said, actually, 99% on the lab benches. But let's call it more than 95% of the research budget is going to the lab bench. And it's for the same reason that 99% goes to the cars and trucks. You need the flexibility to do science. And you can tell this if you walk around at Merck or Pfizer, Takeda and you walk the hallways, you will not see robots. You will see lab bench after lab bench with benchtop equipment on top of it and scientists basically being the human glue that connects all that different equipment and manages to do liquid handling by hand with pipettes and all the things that they do. That is the overwhelming majority of research spending and pharma is doing, again, $40 billion to $60 billion of not development, but research spending every year through that platform. All right? What are we trying to build? We're trying to build that Waymo. What Ginkgo believes we have when it comes to our RAC hardware and then very importantly, the software that runs it is an autonomous lab. It is the flexibility of the lab bench, but the automation of the work cell. And that is, we believe, fundamentally different. It's a much bigger market than the work cell market. The work cell market, again, just like the subway is very limited in terms of the amount of research dollars flowing to it. And so we want to go right at that autonomous lab market. The key technical question, the next slide is how do you get both high automation and high flexibility without having those human hands in the lab, right? And so that's the next thing I want to talk about. What do we actually have to pull off technically to make this a reality? What are people so impressed with when they come visit our lab here in Boston and see what we've built. All right. Also, I don't know if you've noticed, if you follow me on LinkedIn, you've seen I've become a bit of an influencer lately. So this is what it looks like if you're standing at a lab bench, doing your work by hand. And the real major activities is, number one, you're serving as a manual liquid handler. In other words, you are moving small volumes very precisely between different liquid containers to set up an experiment with the right materials in it. Second, you're moving samples, in other words, that liquid you just set up in a plastic tube or whatever it might be in to different devices across the lab. So you are moving samples as the protocol demands across maybe 3, maybe 10 different devices depending on the complexity of the protocol you're doing. And then finally, every time that sample ends up on a device, all those devices, these are all like complicated long-tail scientific devices. They have some settings that you need to set in order to have it do the thing you want it to do. And so you, as a scientist are the one putting those settings in, and you're either doing that with a touchscreen or with sort of third-party software. Okay. All right. So to replace traditional labs, an autonomous lab has to do those same things I just said, that's 1, 2 and 3, reliable liquid handling, material transport and parameterized control of the device. But very importantly, if you think about what one of those labs at Takeda or Merck looks like, in one floor with a bunch of benches and maybe 20 or 30 scientists using it, you're going to have more than 50 devices around that lab that those scientists are making use of, different ones, different days, different ones as part of different protocols. So you've got to be able to put at least 50 devices into one big setup. The other thing that those scientists are doing, when they -- the first scientist gets in the lab in the morning, they do not close the door behind them, lock it and put up a sign that says "lab in use". No one else can come in, right? It's busy. Lab is busy. But on a work cell, like one of those subway system automations that we have in the lab, that's exactly how it works. Once it's in use, you cannot interject yourself into that process and submit a new job. But in the manual lab, absolutely 10, 20, 30 scientists are all walking around that lab, basically sharing the equipment and avoiding each other's usage of the equipment. So if I'm using something in the morning, you'll use it in the afternoon. But other than that constraint, they have access to all that equipment and they can use it in parallel. And then finally, it's very easy to use the lab bench. You don't have to write software programs and things like that. I won't have as much time to talk about it today. But in the coming earnings call, I'll do a little bit of a deeper dive on our software. But one of the things we're really benefiting from is all this investment in coding agents, things like Codex, from OpenAI and Claude code are now allowing human language to turn into pretty complicated software. We want to turn scientific intent into work that runs on automation without scientists needing to code. I think that is going to be very doable, thankfully. And that's number six. It needs to feel like when I go in the lab every day to do my work, I don't have to sit down and write code. You shouldn't have to do that for the autonomous lab. This is really a difficult set of challenges. Work cells today, do those first 3. They deliver liquids. We have liquid handler automation, companies like Hamilton and Tecan have been around for 25 years or great -- more. They're great. Second, reliable material transport can be done with arms. And third, parameterized control is doable. 4, 5 and 6 are not delivered well by traditional lab automation today, but we do have it working at Ginkgo. All right. The first thing to understand about how to deliver 4, 5 and 6 is that a work cell, in other words, that subway is designed around a protocol. So the first thing one of those companies will ask if you're going to build -- they're going to build an automation system for you is, what's your protocol? Are you doing high-throughput screening? That's one of the most common ones, antibody developability, protein production? What is it you're doing, right? And you say, "Oh, I'm doing this, these are the steps. This is the equipment I need, and this is the throughput". And then they'll design a subway that delivers you to that stop. Autonomous labs are not designed around your workflow, but they're rather designed around the equipment because this is exactly what happens when you're setting up a new manual lab at Takeda or Merck. If you're the person in charge of that lab, you're that kind of group leader, you ask your scientists, what equipment will they need to do their work over the next 5 years in that lab. They don't know for sure what protocols they're going to do, but depending on the type of work they're doing, mammalian work, bacterial work, cancers, whatever, they're going to use different types of equipment. So we oriented the design of our robotics hardware, not around a protocol, but around the device. And so this -- here, you can see our RAC automation carts. Inside each cart is a device. In this case, that's a centerfuge, a 6-axis industrial robotic arm and a piece of MagneMotion track. And that track allows you to deliver a sample between connected RACs. So each one of those RACs, their little tracks connect to each other and you can send samples around and deliver them. If you go to the next slide, we can show a video here of samples moving through our autonomous lab here in Boston. And this is actually -- interestingly, this is one of the protocols from OpenAI, right? And so what you can see as this runs is, we have the sample getting put on to the track. That's a 384 well plate in each well of that plate is a set of conditions that were designed by GPT-5. The plates travel on that MagneMotion track. And in this case, they're delivered to that centrifuge, right? And so the centrifuge is going to spin down that sample. So it just happens to be the first step in this protocol. Now it's going to one of those liquid handling devices. So this is what's called an acoustic liquid handler. It moves liquids with sounds. So one of the things that's great about this is it actually can handle smaller volumes at a greater precision than a scientist could do by hand. So we can move nanoliters of liquids around. As a scientist using a pipette by hand, you're kind of limited to microliters in terms of your ability to be accurate. Now we're going to be adding, in this case, DNA to each one of those wells. So the project we did with OpenAI, a piece of DNA was being added to what's called a cell-free mix of reagents. And the idea is that cell-free mix turns that piece of DNA code into a protein. And the protein level is what we're trying to optimize with OpenAI. We're trying to see, could you change the conditions such that you got higher protein production than any scientist had shown before in the literature. So once that DNA got added, we now shake it up, make sure it's well mixed. And then it's going to end up onto an analytical device in order to basically run the reaction and then measure the levels of protein that are coming out of that particular -- of each well in that 384 well plate. And so to give you a sense for the OpenAI project, each time we did a round with the model, we ran 100 of these 384 well plates, collected all that data, gave it back to the model and then the model was able to design the next round of experiments, okay? So that's what it looks like for a sample to move through the system. At the beginning of that video, you would have seen a quick picture of sort of the data coming in, like the particular designs from OpenAI and then the scheduler. That schedule on that one was just running the one protocol. This is what the scheduler looks like when our people -- scientists at Ginkgo have submitted 30 protocols to the system. And so what you're looking at is each row in this is a different device on the system. And then the X-axis is time. So that orange bar in this case is like now, all right? And what is great is we can basically predict the future, right? We know the system knows exactly what piece of equipment is going to be used for what protocol and each protocol is a different color on this chart. What piece of equipment is going to be used for each protocol in the future. And what the scheduler does is if you showed up at Ginkgo as a scientist and you submitted a new job into this into the -- our autonomous lab, you would say, okay, I'm using the centrifuge for 5 minutes. And then I can wait any -- up to 2 hours before I need to end up on the echo and [indiscernible] you would specify with time windows your protocol. The scheduler will check, could you fit in? And this is very analogous to one of those scientists walking around the manual lab asking their benchmates, Hey, when are you going to be done with the PCR machine? How long -- is it okay if I run the HPLC overnight, here something, do you need to get on it, right? Like having conversations about the availability of equipment, except in this case, it's all computer-controlled and computer scheduled, so we can essentially schedule it perfectly. And so as you add more protocols in, there's a complicated algorithm to handle all this. We are the only people in the world as far as I know that are doing anything close to this scale of variable protocols on a single automated system. And that was pretty well confirmed by wide open eyes during the SLAS tour when I was able to show this off to people. Okay. So we go to the next slide. This is just a different color. So each of those protocols, you can see being submitted by a different user at Ginkgo as well. So that's, I think, actually really interesting where we have not just like a large number of protocols, but also a large number of unique users submitting those protocols. That's also very unique in the world. When you have those work cells, there's an automation engineer or 2 who are sort of in charge of it and everything funnels through them. In the case of our autonomous lab here in Boston, we have tens of scientists submitting protocols every day, different protocols from yesterday that are all scheduled simultaneously. Okay. So hopefully, that next slide, hope that gives you a picture of how we've checked off the sort of 4, 5 and 6 on that list in terms of many pieces of equipment, all in one setup being run simultaneously in parallel, easy enough to use by scientists who aren't automation engineers. Just note that system that's now 50-plus RACs and Ginkgo started off as, I think, 7 or 8. It's very expandable. In fact, on the next slide, after we finished up at SLAS, we were able to bring over the RAC carts that we had at the conference, it was, I think, 7 or 8 and install them all in a day on the system. So the ability to really grow this system is, I think, again, unique when compared to traditional sort of subway style automation. All right. So what's the value prop to customers? There's 3 things, I think, that the -- like a large biopharma or a national lab would get excited about. First, save overhead costs by closing your traditional labs. This is one of the things I'm most excited to do this year with our CRO or kind of research services that we run on -- across all our labs at Ginkgo. As I move more and more of that work onto the autonomous lab, I can shrink the footprint of my labs, which saves me in EHS costs, saves me in rent, saves me in all these different things that you have to carry when you're running these labs. Second, it increases the research productivity of your researchers. So right now, a lot of their ideas are ultimately bottlenecked by the amount of time they have to spend in the lab. We want to really open that up and get much more data per research dollar out of your scientists. And then finally, like we did with OpenAI, you can have AI scientists run what are kind of in the industry called lab-in-a-loop experiments, where the AI model is designing experiments, they're running on the autonomous lab and data is going back. And so we're seeing a lot more interest in that from pharma companies as well. All right. Okay. So the last section I want to talk about is how are we going to sell these autonomous labs, and there's sort of 2 ways we're going to do it. One, we will place a system like we did Pacific Northwest National Labs. We will place it at a customer site. We'll sell CapEx. We'll sell basically service fees, both for the software and for our maintenance of the equipment. And in the future, I could see us even selling things like reagents and consumables and things like that to the users of our system that are sort of automation specific. Additionally, we have this big autonomous lab in Boston that we can offer services on top of. All right. And so what's like the overall market potential? This is back to that 1% on the subways, 99% in the cars. The overwhelming majority of research spending, that $40 billion to $60 billion in pharma, the $40 billion plus in -- from the government and so on, that's all funneling through ventures today. And that's before we also -- the other big industry we haven't talked about at all is sort of diagnostics, and I also see opportunities there as well. So all of that bench labs spending, I think, ultimately has the opportunity to funnel through our platform if our autonomous lab is able to replace the bench. The way we're going to get there is we're going to start by commercializing in 2 ways: First, build those autonomous labs for customers; second, run the cloud lab. All right. So cloud lab services. Two of them are ones you've already heard about. So our Solution services. This is where Ginkgo scientists use our autonomous Lab to deliver a research outcome to a customer. So our deals with Novo Nordisk and Bayer Crop Science and Merck and Pfizer and all these people over the years where Ginkgo scientists use Ginkgo's labs to deliver a research outcome. We get a royalty, we get milestones. We can structure these in different ways. We do a lot of work with the government in R&D grants and things like that through our Solutions business. Second, in data points, customer scientists use our autonomous lab. The design what they want to run on it. This is run like a traditional CRO. There's no royalties. There's no milestones. We send them a huge amount of data back usually to their ML team, and they use that to train bio AI models for protein design or RNA design or whatever they might be doing. The third, and I'll have more about this in future earnings calls, but we'll be announcing this soon, is our cloud lab offering. And so what we're going to have here is customer scientists outsourcing small amounts of lab work directly to our autonomous lab. So think like a $50 order or a $200 order where the actual experiment will get run on the cloud lab and data will go back to the scientist. And I think this is a great way for scientists who are curious about what it's like to engage with autonomous labs to sort of try it before they buy. And there's lots of things to try there, different ways to bring it to market. You'll hear from us coming up on that. I'm really excited about it. Just to say, we're not new to the Solutions business. We've done 250 partnerships in the last 10 years. We're continuing to sign these every quarter. We're doing a lot of business with the government and large pharma are really the 2 areas where we see the most of this, but also agriculture. Industrial biotech has been a lot harder since 2022, basically. But ag, pharma and government still will sign up for Solutions deals. The other area that's grown really well for us over the last year, and I want to give a shout out to the Datapoints team at Ginkgo is we've been growing this business where we run our robotics to generate big data sets against the designs of customers. And this is a business that got started 1.5 years ago. We've now worked with 10 of the top 20, I think, or 30 top pharma customers just in the first year we launched this thing. So people are really excited about it. It's a good fit. We've actually released a bunch of public data sets. If you go to the Datapoints website, you can download some of the largest data sets for drug seek and for antibody developability and things like that. Next slide. I think that we've also done a really nice job being a community builder here. We've launched a developability competition. We have a virtual cell pharmacology initiative where we do free data generation as part of building up a big public data set. So really, I think if you're interested in this area, if you're doing bio AI, definitely check out data points, come to some of our events. The last point I'll mention about us running the CRO labs is that our scientists using our big autonomous lab in Boston is a little bit like the Waymo engineer 5 years ago going through Palo Alto, sitting in the driver seat with their fingers like this, right next to the steering wheel, like ready to grab it if the car turns into a mailbox or something. They are the first ones to push lab autonomy to the frontier, right? When you saw those 30 protocols running on a system there, we were the first people to do that, right? And so things break. And that allows us to very quickly speed our development cycle on the autonomous lab compared to companies that really just focus on robotics or on software or things like that because we are doing wet lab research on our own infrastructure, we are learning really fast about what works and doesn't and very importantly, about how to onboard scientists into autonomous labs. Like that is a cultural change, right? And so it involves technical tools to make that easier and faster so that they can still get their very important work done, but they can run it overnight. One of the things that our scientists have really enjoyed doing. If you watch like the ramp of protocols getting on to Nebula, our big autonomous lab here, it spikes in the afternoon and then people whose experiments run overnight and they come in the morning to data, which it's been a while since I've been at the lab, but that's sort of the dream is to show up in the morning with a coffee to a fresh data set. So I do think scientists get really excited about this as we bring the barriers down. But again, Ginkgo's team gets to be the guinea pigs so that our customers of the autonomous lab end up being able to see what's possible and also have a lot of that debugged in advance. I'll talk a minute about our OpenAI project. If you're sitting in the back of a Waymo, an autonomous car, you tell it where to go. Once you close the door and get out or get out and close the door, Waymo's AI takes over and it tells the autonomous car where to go. So what the autonomous car is solving is the problem of replacing the manual driving, not the directing. Same idea here. When we're solving the autonomous lab problem at Ginkgo, what we're solving is the manual lab work. not the directing of what lab work to do. And that could be done by scientists as is done every day at Ginkgo, as you saw all those protocols on the scheduler, but you can also try to have those experiments run by AI scientists. And so our project with GPT-5, like I was mentioning, we were doing 100 of these 384 well plates. We're giving that data back to the model. It was interpreting the data and then sending in new designs. We have a great archive paper on this, if you Google, if you look at the OpenAI blog post, you can find it. We learned a number of things about this. I think we did some really smart stuff with OpenAI here. I'll just give you one quick vignette. So the model is designing the parameters of the experiment, but we don't let it just run anything. We had what's called a pydantic model, which was basically a software-defined set of rules, and we have open source this, you can download it, where GPT-5 submitted designs into that, and it had to pass a series of tests for us to be willing to run it. And if it didn't, we would tell, it failed and it would redesign until it met the test. So simple things, 384 well plate submit 384 wells. The volume of the well is this much liquid, do not exceed that amount of liquid or else it's going to spill everywhere, right? But more complicated things, do your experiment and quadruplicate because we want to publish a paper about this and scientists are going to want to see replication. Include a set of standard controls, experiment to experiment, so we can fairly compare how you're doing over time. So we put those rules in. But then within the experimental wells, like the rest of the plate, as long as it put the right amount of volume, it could do whatever it wanted. And so across 500 plates in the experiment, we had only 2 that we thought were just total nonsensical designs. And one of them was a problem with our pydantic model where GPT-5 designed negative volumes of certain reagents to try to squeeze more reagents in, under the volume limit. Obviously, you can't do negative volumes. So we added that to the model and it learned not to do that. So really, I think this is the first demonstration of really more open-ended experimental work, beating state-of-the-art. There's definitely really great ways to take this work in the future that we're going to continue following up on. OpenAI basically used us as a cloud lab, right? They paid us to do the data generation and their model was able to send and receive commands and data back from our autonomous lab in Boston. All right. I'm going to sort of end on this next one or nearly. Ginkgo is the right company to bring autonomous labs to market at scale. I deeply believe this. This is now apparent to me, in particular, over the last quarter. We have our cash burn under control. That's why we wanted to guide to that and keep the team and have investors understand how much we plan to invest in this. We have extensive practical experience automating lab work. This is what we have been doing the last decade plus at Ginkgo. We know what is hard. We know what it takes to move bench work on to liquid handlers to what are the little tips and tricks associated with each benchtop device when you run it at high throughput and high capacity. All of that information is getting embedded into models and our software to make this really just work magically for scientists as they move to the autonomous lab. I think we're the only ones to do it, and it is a dead mission fit for making biology easier to engineer. I'm convinced that the #1 problem in that space right now is the lab work. We are just not able to try enough genetic designs to get good at genetic engineering. That's not Ginkgo. That's the whole industry. Next slide. do you want to mention because I'm sure some folks don't tuning in are scientists or potential customers and so on. A lot of times, we also hear from scientists, hey, should I be worried about this? Obviously, part of my job is working at the lab and generating this data. I really like this old advertisement from 1951, IBM, and it talks about how the mechanical calculator or electronic calculator, I should say, is going to have to do the work of 150 extra engineers at your company. And what I love about this is, if you're not familiar with that device, [ you're ] the younger folks or whatever on this call, and that is a slide rule. So this is back when computation was done manually. And this device, this predates general purpose computers. This was literally just a device that like added and subtracted and divided the basic arithmetic was going to do the work of 150 engineers, and you might say that device will replace 150 engineers. Now of course, you fast forward 70 years, and there are 100x more engineers than there were back in 1951. And that's because the return on investment on what is in the head of people who understand engineering increased dramatically on the other side of the automation of the manual work of computation. And if you go to the next slide, I very much believe that will be the case for the manual work of laboratories. What is -- it is insanity that we take people who are PhD caliber, understand all the biology, like all the ins and outs of these ridiculously complicated biological systems. They have to understand human biology, 18 other things. And while they're at it, they have to be extremely careful laboratory technicians in order to move liquids and do this work with great fidelity to even be able to try and test and hypothesize their experiments. We need to divide those 2 things just like computation did back in the 1950s. And if you do that, I assure you, you will get many, many more genetic engineers, many, many more scientists than we have today when our ROI is limited by the manual work at the bench. And we just got to do it, and Ginkgo is going to do it. So please put down your pipettes and join us if you're interested. All right. So next slide. That's my e-mail up there. As always, feel free to e-mail if you're excited about this stuff. I appreciate the time today and happy to take questions.

Thanks, Jason. As usual, I'll start with a question from the public and remind the analysts on the line that if you'd like to ask a question, please raise their hands on Zoom, and I'll call on you and open up your line. Thanks, everyone. Thanks, everybody. All right. Let's get started. So the first question that we have is from BBAGGUE, and this is on X. With the planned expansion of RACs capacity from roughly 50 to 100 units at the Boston facility, could you help us understand how this increased capacity is expected to translate into 2026 revenue? Specifically, what portion of that contribution do you anticipate will be recurring in nature, for example, software, operations, consumables versus project-based services?

Yes, I can take that. So for starters, the RAC expansion in part is to, again, be able to move -- we have extensive labs here where there's a variety of different levels of automation. There's sort of walk-up automation where a person is going up to like a liquid handler, the liquid handler does some automated work, then you take that sample somewhere else in the lab. Work cell automation, which is that subway I was talking about and then benches for some of our lab work, we're still at the bench, right? And so we want to take all that work and basically shift it on to Nebula onto that big 100 RAC system in the coming year. So that's sort of the point of it. Now on top of that system, we'll do those services, right? So we'll have our data point service, our upcoming cloud lab service and then our Solution service. So I can speak to sort of the repeatability of those services. The way Solutions deals work, those are usually multiyear R&D deals. So there's some reproducibility like across a deal, like we do a big program for ARPA-H, our long-standing partnership with Bayer Crop Science that's been going on for 5 or 6 years. So you have some repeatability inside a contract. But each time we do a new contract, that is hunting for a new project. Datapoints is a bit different. We are starting to see now, as I mentioned in the call, we're in 10 of the top -- again, I think it's 20 or 30 pharma companies where we have -- that is becoming more repeat business as we're able to basically build trust with those partners that we can serve as that outsourced data generation for their teams. The cloud lab one, we'll see. I mean that's a new experiment where I want to go after that smaller batch work from scientists at the bench. I think if you look at more narrowband CROs, the folks that like build DNA, express proteins, these sorts of things, I think you do see a lot of repeat business once someone has confidence with a vendor. We're obviously trying to do more flexible work, and that will be a new experience. We'll see how it goes. But I'm hopeful that would also look like repeat business on the platform. So of the 3, I think Solutions is the one where you're really off hunting each time to add new research partnerships, but the other 2 are a bit more repeat business. And then just to mention it, that's all on the side of using our system in Boston, which what you asked about. But of course, we're also selling our system. So like we sell that system to a PNML or a pharma company or whomever. When our robotics go in, there's an initial spend on CapEx, but then we have a service and software license that's ongoing over time that is also repeat, more like a SaaS business. And then if you were to -- if we had like sort of specialized reagents, some high throughput things, as the system was used, that would also be repeat business as well. But the initial CapEx will be onetime.

Thanks, Jason. Our next question is from Brendan at TD. He actually has 2 questions. And so I'll start with the first one. The first one is, how should we think about U.S. onshoring of manufacturing as a potential tailwind to RACs revenue growth? And what do you think Ginkgo will need to do to maximize share of this trend over the next couple of years?

Yes. So on the manufacturing side, we have been seeing interest for -- on the sort of manufacturing QC, right? So again, what our systems are doing [ in a ] typical manufacturing environment, you're going to be doing production in larger tanks. The RACs are really about integrating laboratory benchtop equipment. Now you do have a bunch of laboratory benchtop equipment in manufacturing plants, and it's used to do quality control across batches, sometimes associated with even kind of like semi diagnostics work associated with following up on patients over time in a lot of these drugs. So there's actually a decent amount of lab work tied to post clinical sort of like once a drug is on the market, that does keep going in a repeated way. I think our sweet spot there would be being able to handle many different QC protocols on one big system, right? So again, the strength is complicated protocols, multistep, but today, you may be doing at the lab bench. You often have folks at those research centers that are part of the kind of manufacturing team and so on, not sort of open-ended research PhD scientists. And so being able to do sort of like the latest type of assay or something that -- being able to deploy that out as a QC step, I think the RACs open up the door for that. So we are talking to some customers about putting our automation into manufacturing sites. So I think there may be a little bit of tailwind there. We'll see.

Cool. Next question is, how is Ginkgo's Datapoints offering being received among customers? Are there any material tailwinds that you expect for this part of the business over the next 12 months?

Yes, really well. I think we're kind of finding the sweet spot in providing -- so like what's going -- I guess I'll just make this quick point on the AI side, right? So there's sort of 2 halves to how AI is impacting the biotech industry. One is what I just spent a lot of time on the call talking about, which is reasoning models and coding models, the same sort of models that everybody is using in basically any information technology space are going to impact the ability for scientists to use autonomous labs and robotics in the lab. That's all being made easier by reasoning models. Separate from that, there are bio AI models. And the most famous one of these is AlphaFold that Google came out with, which was a model trained on not human language, not human reasoning, but rather biological language and in particular, in that case, like amino acid sequences from proteins and the structure of those proteins. So there's a lot of work going on in that area. And in order to build those bio AI models, you need to generate very large data sets with sort of a variety in that case, say, of proteins and their structures, but there's many other things in functional genomics, antibody developability, other areas where pharma companies are asking us to make those big data sets for their ML teams. That side of the house has gotten tailwinds. There was a lot of -- if you're at the JPMorgan conference this year, folks like [ Chai Bio ] announced their partnership with Lilly and [indiscernible]. There's a whole bunch of companies in the start-up side who are starting to partner with the large pharmas because they have great bio AI models. And the reason they have great bio AI models is they have proprietary data. It's not just how smart they are at the modeling, it's that they've been generating these large data sets. So that's sort of opening people's eyes up and I think creating a little bit of a wave there. And we're definitely the right, I'd say, the leader in providing data sets to again to the large pharma, large biotechs, ML teams that are all ready to go for training and everything else. We can do the robot half and the data cleanup half and they can focus on the biological modeling. So it's been good, I would say. Yes, I'm excited about it for this year.

All right. Next question is from at [indiscernible]. This is on X. The question is about basically not just the utilization of racks, but the manufacturing and deployment of racks. And so the question is, similar to how Tesla used the giga press to dramatically improve manufacturing efficiency. Is Ginkgo exploring any specific strategies, technologies or methods to significantly enhance the production efficiency and scalability of RACs?

Yes, it is something we are starting to think about. I mean, mainly because those things take time to put in place. We did make some good decisions. So over the last 4 years or so, I guess, since we acquired Zymergen where this technology started. We actually did do a like a generational upgrade to the hardware of the RACs and made them, and that design change was not really about, in fact, our old RACs that we had back in the day Zymergen needs are -- the new ones are compatible, but the system has many less components. And that was all done for design for manufacturability for exactly this reason. We make them today in San Jose. We do final assembly in our -- through a partner, and then we do final assembly and integration with the third-party equipment at our site in Emeryville, California. As we were to scale up and selling more and more of these, I think you will see us invest in like basically larger partners to repeat that manufacturing process. But even as we have it now, we can actually scale pretty decently on this. So it's something I think we want to plan ahead for, but it's not the immediate problem we have.

All right. I think that that's all that we have for tonight. So of course, if anyone has any questions in general, they can always e-mail us at [email protected]. Jason also put his personal e-mail up there earlier, so you can message him. And yes, thank you, and have a great night, and I hope everyone has a great quarter.

Thanks, everybody.